Python 2.7.16 |Anaconda, Inc.| (default, Sep 24 2019, 16:55:38)

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IPython 5.8.0 -- An enhanced Interactive Python.

? -> Introduction and overview of IPython's features.

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help -> Python's own help system.

object? -> Details about 'object', use 'object??' for extra details.

In [1]: runfile('/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py', wdir='/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts')

187

task_manager

initial_guess

task_manager

initial_guess

run_mcmc

_initialize_chains

_step

_get_Rhat

_get_psi

INITIAL GUESSES 0.5111092913068727 0.0 1.0647661268160904 0.0 0.3661724311703048 1.0 0.28025477707 0.6826005297968233 2.693995830668935

INITIAL GUESSES 0.5684735546407172 0.0 1.0295624116885191 0.0 0.2921073582237324 1.0 0.335403726708 0.9764299511143792 1.8498572995792366

step

INITIAL GUESSES 0.5892446151091898 0.0 1.0229320851843915 0.0 0.2549249160948012 1.0 0.37012987013 0.4925942992762194 4.884615670513272

step

INITIAL GUESSES 0.48894834358992223 0.0 1.0662581899390389 0.0 0.35987704074262133 1.0 0.277777777778 1.4495517216406724 1.974955986818289

step

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:597: RuntimeWarning: divide by zero encountered in log10

scatter1 = ax1.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.

warnings.warn(message, mplDeprecation, stacklevel=1)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:646: RuntimeWarning: divide by zero encountered in log10

scatter2 = ax2.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1186: RuntimeWarning: divide by zero encountered in log

prob_arr[j] = np.log(self.piBeagle[i,j])+multivariate_normal.logpdf(values,mean,cov)

500

/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

/opt/anaconda2/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1745: RuntimeWarning: divide by zero encountered in double_scalars

x = np.asarray((x - loc)/scale, dtype=dtyp)

1000

scatter1 = ax1.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:646: RuntimeWarning: divide by zero encountered in log10

scatter2 = ax2.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

1500 15000

1500

1500 15000

1500

1500 15000

1500

1500 15000

1500

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

2000

scatter1 = ax1.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:646: RuntimeWarning: divide by zero encountered in log10

scatter2 = ax2.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

2000

scatter1 = ax1.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:646: RuntimeWarning: divide by zero encountered in log10

scatter2 = ax2.scatter(self.xi[idx_z_bin][idx_sort], self.eta[idx_z_bin][idx_sort], c=np.log10((z_good/z_bad)[idx_sort]), cmap=mpl.cm.get_cmap('coolwarm'), alpha=1.0, vmin=-2.5, vmax=2.5, s=100)

2000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2334727035514477 0.0 0.0 0.8846179157134574 0.0 0.0 0.123523474993 1.0 0.544523880099 1.31544969258 0.803224910571 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.268613824224777 0.0 0.0 0.88616667996481 0.0 0.0 0.103934420134 1.0 0.510945956264 1.22610668378 0.800379657025 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.268613824224777 0.0 0.0 0.88616667996481 0.0 0.0 0.120140329796 1.0 0.53191599062 1.22610668378 0.800379657025 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.186158663767324 0.0 0.0 0.8775289382404049 0.0 0.0 0.149798930719 1.0 0.567049596651 1.49067919517 0.915928712321 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.212920937446127 0.0 0.0 0.8797743375038202 0.0 0.0 0.145164801369 1.0 0.500837221942 1.2391169805 0.915928712321 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2109923698648484 0.0 0.0 0.893776443387855 0.0 0.0 0.146279582245 1.0 0.599521712479 1.2391169805 0.796048395242 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2109923698648484 0.0 0.0 0.893776443387855 0.0 0.0 0.146279582245 1.0 0.547789852346 1.2391169805 0.802718736592 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2109923698648484 0.0 0.0 0.893776443387855 0.0 0.0 0.134692475344 1.0 0.549692649955 1.2391169805 0.884872609539 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2109923698648484 0.0 0.0 0.893776443387855 0.0 0.0 0.135534741379 1.0 0.546124669369 1.20467670265 0.884872609539 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2274670962232332 0.0 0.0 0.8967179788723249 0.0 0.0 0.166193154704 1.0 0.538237660482 1.32548205021 0.884872609539 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1475458612175249 0.0 0.0 0.8430771135917673 0.0 0.0 0.076894165245 1.0 0.540590580463 1.27044492427 0.827804397807 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1686534149887409 0.0 0.0 0.8550070709352257 0.0 0.0 0.0403718918341 1.0 0.571793623946 1.44005034428 0.638814441973 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.244661834412963 0.0 0.0 0.9288060090561132 0.0 0.0 0.054232268982 1.0 0.549868608497 1.32999975095 0.867980499438 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.244661834412963 0.0 0.0 0.9288060090561132 0.0 0.0 0.0730363557652 1.0 0.514417171203 1.50377294989 0.651668125378 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.244661834412963 0.0 0.0 0.9288060090561132 0.0 0.0 0.0730363557652 1.0 0.503036619711 1.50377294989 0.740925648125 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2255365096520834 0.0 0.0 0.9016723420038489 0.0 0.0 0.0464276482907 1.0 0.537865249782 2.05996107325 0.453191698294 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.244661834412963 0.0 0.0 0.9288060090561132 0.0 0.0 0.0789030393337 1.0 0.527493302355 1.50377294989 0.740925648125 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1869772157810337 0.0 0.0 0.876087367148791 0.0 0.0 0.0393492846952 1.0 0.539333442894 1.22358428885 0.828506234093 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1752229606973374 0.0 0.0 0.860001787857014 0.0 0.0 0.0583383712548 1.0 0.514717357022 1.35343938517 0.693950349002 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

2500

Traceback (most recent call last):

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.py", line 334, in __call__

return printer(obj)

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 247, in <lambda>

png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))

File "/opt/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.py", line 131, in print_figure

fig.canvas.print_figure(bytes_io, **kw)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.py", line 2212, in print_figure

**kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 517, in print_png

FigureCanvasAgg.draw(self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 437, in draw

self.figure.draw(self.renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/figure.py", line 1493, in draw

renderer, self, artists, self.suppressComposite)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py", line 2635, in draw

mimage._draw_list_compositing_images(renderer, self, artists)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/image.py", line 141, in _draw_list_compositing_images

a.draw(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/artist.py", line 55, in draw_wrapper

return draw(artist, renderer, *args, **kwargs)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/legend.py", line 775, in draw

bbox = self._legend_box.get_window_extent(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 266, in get_window_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 388, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 259, in get_extent

w, h, xd, yd, offsets = self.get_extent_offsets(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 458, in get_extent_offsets

for c in self.get_visible_children()]

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/offsetbox.py", line 831, in get_extent

bbox, info, d = self._text._get_layout(renderer)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/text.py", line 309, in _get_layout

ismath=ismath)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.py", line 236, in get_text_width_height_descent

s, fontsize, renderer=self)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 501, in get_text_width_height_descent

dvifile = self.make_dvi(tex, fontsize)

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/texmanager.py", line 362, in make_dvi

with Locked(self.texcache):

File "/opt/anaconda2/lib/python2.7/site-packages/matplotlib/cbook/__init__.py", line 2529, in __enter__

raise self.TimeoutError(err_str)

TimeoutError: LOCKERROR: matplotlib is trying to acquire the lock

'/Users/lester/.matplotlib/tex.cache/.matplotlib_lock-*'

and has failed. This maybe due to any other process holding this

lock. If you are sure no other matplotlib process is running try

removing these folders and trying again.

2500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.093753557872824 0.0 0.0 0.8384646479303594 0.0 0.0 0.0957589159903 1.0 0.500979502282 1.70902313784 0.625514993232 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0846516952589278 0.0 0.0 0.8256244046908853 0.0 0.0 0.0767422783036 1.0 0.534132481664 1.65394603649 0.685299930911 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1980575461149359 0.0 0.0 0.8636862723350853 0.0 0.0 0.0507526998509 1.0 0.522403427362 1.47468668939 0.780675342937 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.194223644139506 0.0 0.0 0.8611509704138683 0.0 0.0 0.0450670127305 1.0 0.535942535937 1.38592716287 0.685658061186 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.194223644139506 0.0 0.0 0.8611509704138683 0.0 0.0 0.0450670127305 1.0 0.506730213696 1.51327708251 0.821112735274 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1509539088111398 0.0 0.0 0.7805635964900072 0.0 0.0 0.0225489612934 1.0 0.501905060605 1.92319093353 0.904263151617 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1509539088111398 0.0 0.0 0.7805635964900072 0.0 0.0 0.0225489612934 1.0 0.527306305714 1.60265468144 0.640275262687 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.175443586472779 0.0 0.0 0.8371487011416725 0.0 0.0 0.0267100761394 1.0 0.500993447638 1.56740193346 0.896113156335 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1803676311603257 0.0 0.0 0.8376364851920813 0.0 0.0 0.0310201168538 1.0 0.516002252043 1.31882759582 0.835094736336 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1803676311603257 0.0 0.0 0.8376364851920813 0.0 0.0 0.0348632773373 1.0 0.630819436497 1.29907082676 0.835094736336 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1319518451913047 0.0 0.0 0.8371566431461075 0.0 0.0 0.0578347013813 1.0 0.611400054246 1.4415806219 0.797919502442 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1068: RuntimeWarning: divide by zero encountered in log

scale=outlier_sigma_prop)))

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1360719541325848 0.0 0.0 0.8297607963641754 0.0 0.0 0.0439117275484 1.0 0.503390685793 1.68092341931 0.521487349213 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

3000 15000

3000

3000 15000

3000

3000 15000

3000

3000 15000

3000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.197646108830581 0.0 0.0 0.8484550915204558 0.0 0.0 0.0253451778113 1.0 0.535092005556 1.6522083222 0.683017130905 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1972729212147986 0.0 0.0 0.9082464400269418 0.0 0.0 0.10432496916 1.0 0.506883180778 2.00239564781 0.390806462018 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.168822345498329 0.0 0.0 0.8335905017815118 0.0 0.0 0.123427697498 1.0 0.500456608064 1.76479528103 0.724089869024 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2118900718778471 0.0 0.0 0.8693889568219854 0.0 0.0 0.0245623803828 1.0 0.500316917467 1.67252729057 0.673553023581 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2375538238343526 0.0 0.0 0.8881065135603133 0.0 0.0 0.0166873543465 1.0 0.501564148373 1.47026135326 0.890872651622 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1429124658091472 0.0 0.0 0.8466537450261314 0.0 0.0 0.129221860923 1.0 0.533495332605 1.47668690041 0.867720910562 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1429124658091472 0.0 0.0 0.8466537450261314 0.0 0.0 0.129221860923 1.0 0.608285632686 1.1959694803 0.745238549111 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1429124658091472 0.0 0.0 0.8466537450261314 0.0 0.0 0.126643011498 1.0 0.518141016595 1.24455367018 0.745238549111 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1668689489817725 0.0 0.0 0.8506779831880058 0.0 0.0 0.102057514455 1.0 0.505898384744 1.255631444 0.966166776139 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1224944558433159 0.0 0.0 0.7491665016447265 0.0 0.0 0.111232924896 1.0 0.515888381311 1.33989479285 0.788633681654 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1224944558433159 0.0 0.0 0.7491665016447265 0.0 0.0 0.114194445508 1.0 0.624988541375 1.33989479285 0.919445509768 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.161458127425367 0.0 0.0 0.8310731798942113 0.0 0.0 0.0743257420062 1.0 0.501810888781 1.25546369944 0.807772674515 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.157860694441121 0.0 0.0 0.8592089607064533 0.0 0.0 0.05862173257 1.0 0.624096124047 1.50460082574 0.760213251751 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.167782880569492 0.0 0.0 0.8847640711698068 0.0 0.0 0.0554871141802 1.0 0.502075452505 1.68228467848 0.789527136882 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1703905510529713 0.0 0.0 0.8865018713421733 0.0 0.0 0.0445444279407 1.0 0.538860070523 1.20606539431 0.908788085972 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1535639252002987 0.0 0.0 0.8679994079259062 0.0 0.0 0.0445444279407 1.0 0.51247079687 1.20606539431 0.852767079441 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1535639252002987 0.0 0.0 0.8679994079259062 0.0 0.0 0.042818826836 1.0 0.52039640315 1.03390127637 0.872268197001 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1535639252002987 0.0 0.0 0.8679994079259062 0.0 0.0 0.0399336967535 1.0 0.5434379849 1.32605985215 1.00608939502 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

3500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.180167309087016 0.0 0.0 0.8975455618646512 0.0 0.0 0.0239085595092 1.0 0.55946884207 1.65096905244 1.22758787819 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1760954732156734 0.0 0.0 0.8873354934243531 0.0 0.0 0.0334288628163 1.0 0.555510247181 1.63845212534 0.95272338044 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1565081507244783 0.0 0.0 0.8729960488813893 0.0 0.0 0.0408912627569 1.0 0.528960614315 1.32188822847 0.918787036604 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1565081507244783 0.0 0.0 0.8729960488813893 0.0 0.0 0.0408912627569 1.0 0.576133736713 1.46603224504 0.918787036604 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1565081507244783 0.0 0.0 0.8729960488813893 0.0 0.0 0.0457099217933 1.0 0.506976390995 1.46603224504 0.880872297525 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.149257940608014 0.0 0.0 0.8789722840152405 0.0 0.0 0.0410147528523 1.0 0.521520193103 1.35385505893 0.79108907708 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.149257940608014 0.0 0.0 0.8789722840152405 0.0 0.0 0.0445590658681 1.0 0.58331205501 1.35385505893 1.03670932524 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.149257940608014 0.0 0.0 0.8789722840152405 0.0 0.0 0.0389623755396 1.0 0.503052879714 1.51335156933 0.849170472637 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1529195465523503 0.0 0.0 0.8712477591514955 0.0 0.0 0.0591467387563 1.0 0.519993084393 1.4369282566 0.835861835918 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1529195465523503 0.0 0.0 0.8712477591514955 0.0 0.0 0.0601200550463 1.0 0.546681411298 1.4369282566 0.835861835918 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1187265754490148 0.0 0.0 0.8242907910086028 0.0 0.0 0.0330848542003 1.0 0.546151202091 1.13852011194 0.978754289375 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1187265754490148 0.0 0.0 0.8242907910086028 0.0 0.0 0.0351528690467 1.0 0.507401806878 1.14627688464 0.978754289375 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1187265754490148 0.0 0.0 0.8242907910086028 0.0 0.0 0.0351528690467 1.0 0.53369501898 1.51537646793 0.760237779689 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1121208793382302 0.0 0.0 0.8099884854723663 0.0 0.0 0.0351528690467 1.0 0.507844693539 1.50073390612 0.785893954399 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1121208793382302 0.0 0.0 0.8099884854723663 0.0 0.0 0.0245310896294 1.0 0.524419258597 1.34428027851 0.83543807475 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.023513306769 1.0 0.553705791667 1.467533291 0.740029042037 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0189164382928 1.0 0.520337005953 1.31134590029 0.824814813741 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0189164382928 1.0 0.506322486048 1.25010317323 0.824814813741 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0141402814891 1.0 0.517782862992 1.15735579853 0.726059741892 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0124695231674 1.0 0.531135056861 1.36378534029 0.796382326833 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.00780701662437 1.0 0.522056011716 1.35011815499 0.733123663703 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0069637817299 1.0 0.509515505747 1.46979014447 0.981906799113 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0069637817299 1.0 0.549033571327 1.20008153683 0.964947164654 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.00622242362594 1.0 0.507995121521 1.16835966497 0.787227943859 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0100776796613 1.0 0.525201499092 1.28434547898 0.884680631722 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0112437643675 1.0 0.526571843461 1.33812143517 0.884680631722 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1086612741388993 0.0 0.0 0.8027182965179778 0.0 0.0 0.0112437643675 1.0 0.546061004011 1.34607237666 0.884680631722 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

4000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1053667802198002 0.0 0.0 0.8032469632107759 0.0 0.0 0.0112437643675 1.0 0.557717093629 1.23971375449 0.884680631722 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1053667802198002 0.0 0.0 0.8032469632107759 0.0 0.0 0.0112437643675 1.0 0.554865667107 1.40789399907 0.900805932173 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1053667802198002 0.0 0.0 0.8032469632107759 0.0 0.0 0.0105314627313 1.0 0.502532255978 1.23393496635 0.816241052069 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

4000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1053667802198002 0.0 0.0 0.8032469632107759 0.0 0.0 0.0122594005656 1.0 0.556908272112 1.47917388456 0.960776822922 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1053667802198002 0.0 0.0 0.8032469632107759 0.0 0.0 0.0141618664009 1.0 0.634524851173 1.28519127646 0.981229199464 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

4000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1053667802198002 0.0 0.0 0.8032469632107759 0.0 0.0 0.0235881518425 1.0 0.52290173199 1.25025431661 1.00053148901 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1125346403684466 0.0 0.0 0.8142563945082191 0.0 0.0 0.0235920753035 1.0 0.554206316144 1.13076543604 0.964343030906 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1125346403684466 0.0 0.0 0.8142563945082191 0.0 0.0 0.017582969547 1.0 0.55285669565 1.38445031953 0.787336372136 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1406255179422933 0.0 0.0 0.8436387764370779 0.0 0.0 0.0241974377201 1.0 0.515139292201 1.6238072404 0.759249523959 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

4500 15000

4500

4500 15000

4500

4500 15000

4500

4500 15000

4500

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1068: RuntimeWarning: divide by zero encountered in log

scale=outlier_sigma_prop)))

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.175397358192516 0.0 0.0 0.9032644052876059 0.0 0.0 0.0709740482649 1.0 0.510837409636 1.78416544031 0.645478281664 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1730789728968467 0.0 0.0 0.8626843656041694 0.0 0.0 0.0492608615 1.0 0.590993169017 1.59014634584 0.673349235293 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1608239151963629 0.0 0.0 0.8292376150808033 0.0 0.0 0.0910571177208 1.0 0.500838523495 1.41866984314 0.930237365366 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1699740249468356 0.0 0.0 0.8583531672884531 0.0 0.0 0.0681435998048 1.0 0.52322368413 1.46241729868 0.956707658116 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1626682154676309 0.0 0.0 0.8504960136483177 0.0 0.0 0.0676803813507 1.0 0.5457269072 1.77056630068 0.83082380781 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1321195571290992 0.0 0.0 0.8384359208322516 0.0 0.0 0.109182533361 1.0 0.507380266171 1.48473672386 0.705132295291 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

5000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1195068009131954 0.0 0.0 0.7881314131895091 0.0 0.0 0.0988274191549 1.0 0.555551448163 1.53772729261 1.01887370209 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

5000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2060397277715504 0.0 0.0 0.8676167152714185 0.0 0.0 0.0524082913433 1.0 0.60927708067 1.41455660968 1.02843891762 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.215643181746332 0.0 0.0 0.8842838744275946 0.0 0.0 0.0437970489335 1.0 0.508630902876 1.40124800395 0.863306021907 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1477934464791995 0.0 0.0 0.8211287138277004 0.0 0.0 0.131485270962 1.0 0.612604718608 1.54456910495 1.01809953636 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1295287233327185 0.0 0.0 0.812538723807815 0.0 0.0 0.044996976363 1.0 0.519203716067 1.3628345751 0.983246906434 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1607503198229214 0.0 0.0 0.815393447091585 0.0 0.0 0.1289363861 1.0 0.522539307367 1.90727220786 0.531986984475 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.079760230976855 0.0 0.0 0.779748904560251 0.0 0.0 0.152481465509 1.0 0.523528596029 1.72464222949 0.996301658875 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1054160703500289 0.0 0.0 0.7807590265205233 0.0 0.0 0.146123010259 1.0 0.560392643154 1.42208170628 0.821645169684 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0933633286119304 0.0 0.0 0.77878201466115 0.0 0.0 0.146822724677 1.0 0.566989285763 1.42208170628 0.821645169684 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1994379793333283 0.0 0.0 0.8139753678262425 0.0 0.0 0.151959240191 1.0 0.523071664705 1.69772136428 0.7817818135 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0813110150719856 0.0 0.0 0.8241749011082848 0.0 0.0 0.0891516833488 1.0 0.568573486949 1.5269667814 0.734519475617 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.04023282558849 0.0 0.0 0.7302814080624981 0.0 0.0 0.103250141181 1.0 0.5020783378 1.8399119346 0.603988369784 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0885892798559484 0.0 0.0 0.7965967492100035 0.0 0.0 0.0743649442635 1.0 0.599245723354 1.5847120709 0.724023398977 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0694447832286051 0.0 0.0 0.7839930887611908 0.0 0.0 0.073726532307 1.0 0.514622351493 1.67586557937 0.568910446061 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0359149190706691 0.0 0.0 0.7318731171851277 0.0 0.0 0.0735757481672 1.0 0.519826516547 1.43950795044 0.805398450115 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0853804306738752 0.0 0.0 0.7976400657662072 0.0 0.0 0.0672240542873 1.0 0.550850671594 1.83098809527 0.611822153939 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1068: RuntimeWarning: divide by zero encountered in log

scale=outlier_sigma_prop)))

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.090726066156526 0.0 0.0 0.8038283233321709 0.0 0.0 0.0510708317116 1.0 0.607076868848 1.59207696769 0.788071865745 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.122380245114643 0.0 0.0 0.8671514426365462 0.0 0.0 0.0634320333447 1.0 0.500778045571 1.61510223359 0.890168844222 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.102655889335182 0.0 0.0 0.7983651554544717 0.0 0.0 0.0441327655142 1.0 0.644400028859 1.87776723891 0.816057243757 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

5500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1352189910100179 0.0 0.0 0.8689987848623969 0.0 0.0 0.0647789376534 1.0 0.629391568088 1.60265787288 0.800180018795 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1447875677474295 0.0 0.0 0.8767825410496175 0.0 0.0 0.0616276508035 1.0 0.640788835085 1.67877803478 0.873347924218 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1317080099157557 0.0 0.0 0.8245850553108666 0.0 0.0 0.0387284510976 1.0 0.517121942646 1.28185983946 0.879665138782 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.119117344181046 0.0 0.0 0.7870134349626408 0.0 0.0 0.132488593589 1.0 0.520921592961 2.0787372348 0.365816538069 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1439194862105146 0.0 0.0 0.8467716654412567 0.0 0.0 0.0403772183558 1.0 0.686921200336 1.63804749709 0.82845512646 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1640464264230101 0.0 0.0 0.8682433986528743 0.0 0.0 0.0418273337319 1.0 0.683260477313 1.68352868246 0.810241838292 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1543793312315522 0.0 0.0 0.8539779502535977 0.0 0.0 0.0331662974116 1.0 0.502682142262 1.63617571127 0.918772286571 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1588288017282706 0.0 0.0 0.8843102869163769 0.0 0.0 0.022152115068 1.0 0.55504556232 1.61536182852 0.719617368174 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1381667887680342 0.0 0.0 0.804123959095609 0.0 0.0 0.066818573803 1.0 0.549508718038 1.674202546 0.774923252819 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0790271715932536 0.0 0.0 0.7438358140782129 0.0 0.0 0.0676397820297 1.0 0.516312920107 1.42911200224 1.06315028318 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0790271715932536 0.0 0.0 0.7438358140782129 0.0 0.0 0.0750073453826 1.0 0.553374641451 1.58692857131 0.95454175262 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

6000 15000

6000

6000 15000

6000

6000 15000

6000

6000 15000

6000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1415699499147647 0.0 0.0 0.8079307556301791 0.0 0.0 0.0630641487453 1.0 0.51646799582 2.15232329515 0.471615101086 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1862097152439697 0.0 0.0 0.8405292424217823 0.0 0.0 0.106263606955 1.0 0.50333758662 1.67169509885 0.600676777623 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1068: RuntimeWarning: divide by zero encountered in log

scale=outlier_sigma_prop)))

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1083334444002806 0.0 0.0 0.7692970339251819 0.0 0.0 0.102694851912 1.0 0.522795495205 1.62940278314 0.714543343775 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2230262587640828 0.0 0.0 0.8785729334680717 0.0 0.0 0.0680300923016 1.0 0.506028590171 1.63012645762 0.825946829922 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2219499503563953 0.0 0.0 0.9041909271006824 0.0 0.0 0.087833486093 1.0 0.515370637266 1.58722427835 0.83414173082 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2460845909716216 0.0 0.0 0.940174893291117 0.0 0.0 0.062765189162 1.0 0.508686415637 1.7473660891 0.840320993626 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1642518914809599 0.0 0.0 0.838678078324274 0.0 0.0 0.104383070941 1.0 0.51412684338 1.73706841837 0.627838486454 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1366685265888234 0.0 0.0 0.8331383593651547 0.0 0.0 0.126422739275 1.0 0.54829295494 1.36937594271 0.686648854867 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1447929652252309 0.0 0.0 0.8179057941581075 0.0 0.0 0.10965938038 1.0 0.532630959172 1.80533359429 0.615442473283 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1803113192744892 0.0 0.0 0.8879086276437055 0.0 0.0 0.041866788039 1.0 0.502643554377 1.9297357338 0.494987011888 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1420763225407013 0.0 0.0 0.8415783941342053 0.0 0.0 0.110547967262 1.0 0.528091137087 1.93309246977 0.652819552936 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1455714107131383 0.0 0.0 0.8619866089466778 0.0 0.0 0.0629091438941 1.0 0.514839751918 1.38841678383 0.766286069747 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1559851849316796 0.0 0.0 0.8711991757723418 0.0 0.0 0.0563959587319 1.0 0.524646759721 1.46781753583 0.621612432607 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

6500

eta acceptanceProb 0 6481 145 -848.1698103963032 -0.9581003638679029 0.0

6500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1477225276852796 0.0 0.0 0.7951273475139485 0.0 0.0 0.0770754662374 1.0 0.503715670606 1.96769190001 0.447759447583 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1101107451082066 0.0 0.0 0.8470041401061316 0.0 0.0 0.058123585131 1.0 0.51644099477 1.50616180636 0.958760115097 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1773983814826754 0.0 0.0 0.8453056136275308 0.0 0.0 0.0750093988866 1.0 0.515471205328 1.6377196275 0.617025197165 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.102953636917508 0.0 0.0 0.820073759931226 0.0 0.0 0.124598432337 1.0 0.530618745915 1.66424474073 0.623822565725 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0894587879079813 0.0 0.0 0.8022031653691662 0.0 0.0 0.0900526781168 1.0 0.520592981148 1.57824850802 0.790751984516 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0667182284834924 0.0 0.0 0.7492591391020309 0.0 0.0 0.098607576552 1.0 0.580389151324 1.20839613924 1.03673297701 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0230313543875296 0.0 0.0 0.692018806486546 0.0 0.0 0.0971222056592 1.0 0.594921184778 1.40219248057 1.0396921085 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 6885 145 -829.6754074261004 0.9579550797279268 0.0

7000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.136462275271277 0.0 0.0 0.8000521901678604 0.0 0.0 0.0786579910488 1.0 0.52634539073 1.49882553906 0.913167642398 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1013004554638866 0.0 0.0 0.7817669868096344 0.0 0.0 0.0996349681211 1.0 0.563810098876 1.37621118402 0.752109985241 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1487476241143595 0.0 0.0 0.7987844998695328 0.0 0.0 0.0801868567483 1.0 0.508257889144 1.40729926849 0.742206557636 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1487476241143595 0.0 0.0 0.7987844998695328 0.0 0.0 0.0932197224015 1.0 0.595288574149 1.76040713984 0.585907977568 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1755315694391149 0.0 0.0 0.8468201808222935 0.0 0.0 0.0215049312545 1.0 0.528875791802 1.15307776542 1.01183303488 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1891313158559829 0.0 0.0 0.8640083028951137 0.0 0.0 0.0266079350713 1.0 0.533234361294 1.34812086966 0.922041185459 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1891313158559829 0.0 0.0 0.8640083028951137 0.0 0.0 0.0266079350713 1.0 0.514123297433 1.45579408487 0.922041185459 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

7500 15000

7500

7500 15000

7500

7500 15000

7500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1316114801744024 0.0 0.0 0.8105034222764633 0.0 0.0 0.134160741282 1.0 0.568327208695 1.7612714393 0.600459985009 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

7500 15000

7500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1606895366757561 0.0 0.0 0.8007380599470798 0.0 0.0 0.120690015358 1.0 0.535963270731 1.42525109891 0.796688667502 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1849577027370255 0.0 0.0 0.7846075771071831 0.0 0.0 0.061246542651 1.0 0.527228895891 1.58589029794 0.629909981024 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1289951356757262 0.0 0.0 0.7733137466872727 0.0 0.0 0.138807173692 1.0 0.547046257742 1.41769295055 0.80460925897 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0490638840602948 0.0 0.0 0.741050054536632 0.0 0.0 0.114528466613 1.0 0.544286326797 0.979087707003 0.916511849578 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 7635 145 -808.8728440060744 -1.247551268359493 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.151901251410264 0.0 0.0 0.8947481021115749 0.0 0.0 0.0681231116011 1.0 0.512200036088 1.50994727139 0.885054862061 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1480330667195267 0.0 0.0 0.7457336905277926 0.0 0.0 0.229040849682 1.0 0.552673677302 1.71917599337 0.778153216485 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0964127774454309 0.0 0.0 0.7280136217727153 0.0 0.0 0.235656669358 1.0 0.510889182852 1.67297572721 0.971166558139 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1410122088478223 0.0 0.0 0.843777378100034 0.0 0.0 0.0499869161512 1.0 0.504377238718 1.4579130749 1.03408716339 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0186529739312118 0.0 0.0 0.7118500095512592 0.0 0.0 0.20983718068 1.0 0.505301491081 1.65917904017 0.869007981108 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

8000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.120734817652622 0.0 0.0 0.7457547548414921 0.0 0.0 0.158110065103 1.0 0.503959709355 1.91476560069 0.559704508669 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0913287732907853 0.0 0.0 0.8065289347323575 0.0 0.0 0.149934166901 1.0 0.543922305189 1.82740447272 0.510893361315 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1948570783666819 0.0 0.0 0.8436404675551378 0.0 0.0 0.14003179837 1.0 0.527212350099 1.7663065818 0.631749478058 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2255607116726217 0.0 0.0 0.8911408760700805 0.0 0.0 0.0932475182934 1.0 0.537579323876 1.59207898985 0.798793164544 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2035159630916006 0.0 0.0 0.8787452127955734 0.0 0.0 0.135183132252 1.0 0.505728173041 1.51107149657 0.571759889338 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1545467182513305 0.0 0.0 0.8512172338507448 0.0 0.0 0.115555010924 1.0 0.565933697192 1.46485061687 0.565886850344 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1401929925386083 0.0 0.0 0.8564441372750373 0.0 0.0 0.0692994107119 1.0 0.536904814814 1.65879447034 0.928154292295 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1354930982405358 0.0 0.0 0.8564219239813186 0.0 0.0 0.0860764973383 1.0 0.56105991826 1.35473520958 0.949728755289 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.104392987558822 0.0 0.0 0.8475233799609704 0.0 0.0 0.0702902533782 1.0 0.619592094963 1.10528808605 0.901537373568 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1061810001677639 0.0 0.0 0.8486875425740189 0.0 0.0 0.0702902533782 1.0 0.531758538193 1.34007547007 0.752938100251 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1061810001677639 0.0 0.0 0.8486875425740189 0.0 0.0 0.0702902533782 1.0 0.586948710258 1.39553566284 0.752938100251 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.083054551949494 0.0 0.0 0.8182830630202691 0.0 0.0 0.0646081060744 1.0 0.524305013759 1.54792792717 0.548269503721 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2332467006127654 0.0 0.0 0.9138455582645741 0.0 0.0 0.0876641563644 1.0 0.525400020181 1.56932078105 0.857767105056 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

8500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0659431023886607 0.0 0.0 0.781488259468534 0.0 0.0 0.0733995909412 1.0 0.530205835993 1.83877094694 0.655307638205 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

8500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1058739633816337 0.0 0.0 0.8238194015523544 0.0 0.0 0.0952894131664 1.0 0.56909880457 1.60995257733 0.770507423161 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1731706946188165 0.0 0.0 0.8140981371894404 0.0 0.0 0.050417996659 1.0 0.53215424214 1.27555161072 0.850669415686 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2084721737463695 0.0 0.0 0.8426560134731083 0.0 0.0 0.0462143552832 1.0 0.634892447635 1.1905671279 0.861208819629 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1282252947921783 0.0 0.0 0.8439443230945676 0.0 0.0 0.0575126800743 1.0 0.587053169382 1.65250667519 0.648923301032 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2084721737463695 0.0 0.0 0.8426560134731083 0.0 0.0 0.0462143552832 1.0 0.60920429886 1.63287140352 0.861208819629 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0533473936991835 0.0 0.0 0.7198763818033765 0.0 0.0 0.11911087471 1.0 0.500761191215 1.50188273188 0.678223451365 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1971794734382326 0.0 0.0 0.8693976794061057 0.0 0.0 0.113482744086 1.0 0.583680520559 1.39840662051 0.912265753933 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2026782669918594 0.0 0.0 0.8883234274258095 0.0 0.0 0.0904386772849 1.0 0.508348064917 1.16007199096 0.904097656133 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2026782669918594 0.0 0.0 0.8883234274258095 0.0 0.0 0.0952058318723 1.0 0.544053450501 1.16007199096 0.904097656133 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2026782669918594 0.0 0.0 0.8883234274258095 0.0 0.0 0.0854099574306 1.0 0.52497421302 1.550719271 0.809764934851 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.242545252223954 0.0 0.0 0.9159274871479715 0.0 0.0 0.0785797074767 1.0 0.562441757133 1.32672794439 0.752785178959 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2521338682909593 0.0 0.0 0.9532234936633137 0.0 0.0 0.100515037352 1.0 0.534830861239 1.43143299634 0.670935000763 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2094820813453382 0.0 0.0 0.9070112779824478 0.0 0.0 0.0912426779108 1.0 0.572589426109 1.48428924961 0.912132110395 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2094820813453382 0.0 0.0 0.9070112779824478 0.0 0.0 0.0912426779108 1.0 0.501614717191 1.49838361628 0.77163232662 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2280062536644616 0.0 0.0 0.8999562410723252 0.0 0.0 0.0411621988701 1.0 0.553301566779 1.29670079435 0.664784675591 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2280062536644616 0.0 0.0 0.8999562410723252 0.0 0.0 0.0390625667221 1.0 0.551893945143 1.16004062072 0.777782127382 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2280062536644616 0.0 0.0 0.8999562410723252 0.0 0.0 0.0460457663783 1.0 0.568809879899 1.16004062072 0.777782127382 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2041343159253792 0.0 0.0 0.8662032273375745 0.0 0.0 0.036458982696 1.0 0.509175445876 1.29332398067 1.08623692633 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 8940 145 -899.2795574917726 0.3143938635643888 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.213728376310266 0.0 0.0 0.8827061915284984 0.0 0.0 0.0405801613268 1.0 0.515093901521 1.6121481659 0.909815634387 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2092260066914193 0.0 0.0 0.8842916177412509 0.0 0.0 0.0448964426714 1.0 0.578455293983 1.85555310961 0.678494134666 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0515326588659593 0.0 0.0 0.789733440410852 0.0 0.0 0.082420506948 1.0 0.542249594221 1.682657791 0.574831537278 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.21040146552333 0.0 0.0 0.9034114378441781 0.0 0.0 0.0426400343562 1.0 0.504064991222 1.68122539985 0.982923761798 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0515326588659593 0.0 0.0 0.789733440410852 0.0 0.0 0.082420506948 1.0 0.513812933853 1.682657791 0.727581936263 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2156960049211747 0.0 0.0 0.8893262428567571 0.0 0.0 0.0375169722063 1.0 0.615432377547 1.34903061402 0.979822056021 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

9000 15000

9000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1370603268070378 0.0 0.0 0.8535501358758233 0.0 0.0 0.112410996063 1.0 0.593729174591 1.99671738057 0.371864656261 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

9000 15000

9000

9000 15000

9000

9000 15000

9000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1076034864439812 0.0 0.0 0.7964819617679021 0.0 0.0 0.109786933998 1.0 0.588049789559 1.57532411576 0.728016837616 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1951511197405529 0.0 0.0 0.8939690581750445 0.0 0.0 0.0713021184608 1.0 0.542553083531 1.78825344029 0.788314942679 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1898475214094946 0.0 0.0 0.8820683138444958 0.0 0.0 0.0754250858329 1.0 0.528573378068 1.73766450066 0.700194783145 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1405357723423326 0.0 0.0 0.8551800775049923 0.0 0.0 0.0619648248455 1.0 0.519834053022 1.1796897829 1.16235039564 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1405357723423326 0.0 0.0 0.8551800775049923 0.0 0.0 0.0739085927734 1.0 0.519953201869 1.18926232862 1.15702619581 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1728206644105952 0.0 0.0 0.8584833014414609 0.0 0.0 0.0660683096999 1.0 0.582142228136 1.57571168442 0.902493897478 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1370323010264909 0.0 0.0 0.8249249369361704 0.0 0.0 0.0597645487918 1.0 0.538168712761 1.40929693076 0.683963171579 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1396406013745137 0.0 0.0 0.8204178335077833 0.0 0.0 0.055920469596 1.0 0.549889343714 1.49557511538 0.858980031065 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.154584698141013 0.0 0.0 0.8422172992670142 0.0 0.0 0.0551739144747 1.0 0.504947997767 1.48137669195 0.868038282459 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1551337246477833 0.0 0.0 0.8532475420645641 0.0 0.0 0.06029277341 1.0 0.553926864657 1.48137669195 0.868038282459 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1551337246477833 0.0 0.0 0.8532475420645641 0.0 0.0 0.0544266522195 1.0 0.639432858011 1.61236786161 0.920172553121 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2138765088628138 0.0 0.0 0.8988219326095476 0.0 0.0 0.114993849758 1.0 0.500792482138 1.59086407681 0.99287368576 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 9252 145 -884.7790578487507 1.977378341862884 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1294031858416793 0.0 0.0 0.810902843605447 0.0 0.0 0.110261423632 1.0 0.563972383635 1.45980575611 0.798255670278 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.092849629026252 0.0 0.0 0.7717119247210517 0.0 0.0 0.122169941896 1.0 0.503599653952 1.57783864587 0.798255670278 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.095572724159913 0.0 0.0 0.7864238336459642 0.0 0.0 0.109323472789 1.0 0.53205571611 1.4376390787 0.779215532591 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1824360653064634 0.0 0.0 0.8308001455127014 0.0 0.0 0.169186077048 1.0 0.517554408228 1.79947464481 0.802092738797 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1207604073245097 0.0 0.0 0.8072938412386979 0.0 0.0 0.0802954909415 1.0 0.501099687304 1.58111194896 0.763664972596 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.133023182471039 0.0 0.0 0.7952813904957748 0.0 0.0 0.161455438174 1.0 0.564140624205 1.88746673171 0.519468076594 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1644805995450405 0.0 0.0 0.877496559886655 0.0 0.0 0.102020911083 1.0 0.513491097793 1.5661121693 0.75278608685 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1795786257890202 0.0 0.0 0.8692119435772266 0.0 0.0 0.0859281407399 1.0 0.503159845057 1.71443690641 0.597630097532 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2222226178625195 0.0 0.0 0.8401206956516412 0.0 0.0 0.101953965049 1.0 0.526123560762 2.17640946327 0.40366502723 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1715748918840418 0.0 0.0 0.8802901211706132 0.0 0.0 0.0488646586658 1.0 0.506566651491 1.35167616548 0.792797894101 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

9500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1707994980161116 0.0 0.0 0.7948484477078329 0.0 0.0 0.0803746076726 1.0 0.542686395308 1.74885838561 0.608483633378 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

9500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1677766866821042 0.0 0.0 0.8767229748151288 0.0 0.0 0.07810968862 1.0 0.528823910648 1.65748874316 0.754230922421 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1677766866821042 0.0 0.0 0.8767229748151288 0.0 0.0 0.0744577168603 1.0 0.513553395322 1.61114759435 0.905064269407 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1089051484699808 0.0 0.0 0.8181792592949299 0.0 0.0 0.0868779168401 1.0 0.503870630139 1.81176231824 0.664057694052 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1436012347756206 0.0 0.0 0.8353060273350779 0.0 0.0 0.086008290177 1.0 0.510168150803 1.60611980751 0.65212510481 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1436012347756206 0.0 0.0 0.8353060273350779 0.0 0.0 0.0986376683988 1.0 0.547092496608 1.60611980751 0.70199384269 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1235817424991696 0.0 0.0 0.8225694360751246 0.0 0.0 0.09957960369 1.0 0.507345159317 1.47704085823 0.666279388214 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1755177833690824 0.0 0.0 0.8035953562630477 0.0 0.0 0.0885954734983 1.0 0.582312919093 1.25769596101 0.875427734226 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1478151143787532 0.0 0.0 0.8427586873030266 0.0 0.0 0.0599006110581 1.0 0.516437179301 2.00887801615 0.766899683318 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1753642809779514 0.0 0.0 0.8676259399092425 0.0 0.0 0.0372634382615 1.0 0.509826476758 1.69940214124 0.717568159198 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1528016519561946 0.0 0.0 0.8354913155023691 0.0 0.0 0.0295535773737 1.0 0.54755441043 1.70877719521 0.749020303592 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

10000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1535366254465926 0.0 0.0 0.8452506484271526 0.0 0.0 0.0308994301213 1.0 0.519847093857 1.66462548523 0.675809699934 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.145525097034897 0.0 0.0 0.8360082219801396 0.0 0.0 0.0182359849422 1.0 0.510836576695 1.59627481506 0.830960637602 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.145525097034897 0.0 0.0 0.8360082219801396 0.0 0.0 0.0429737319327 1.0 0.529843961683 1.38173580047 0.84581320695 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1878114282625256 0.0 0.0 0.8780275137437293 0.0 0.0 0.0535894220784 1.0 0.53396892972 1.48865048871 0.772163523009 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.173269088487545 0.0 0.0 0.8399856594288082 0.0 0.0 0.074309594384 1.0 0.53797469327 1.36328166112 0.72460438453 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.173269088487545 0.0 0.0 0.8399856594288082 0.0 0.0 0.0738394275327 1.0 0.522184817541 1.16267105422 0.811764550673 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1607314743969952 0.0 0.0 0.8344745883681239 0.0 0.0 0.06073826003 1.0 0.560736709064 1.78082977872 0.761037102956 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1946241865181482 0.0 0.0 0.8554337318068292 0.0 0.0 0.0638493554904 1.0 0.51058653812 1.52372196448 0.965093059492 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0582395607365649 0.0 0.0 0.6656552291602422 0.0 0.0 0.21974248153 1.0 0.508078584481 1.71495654989 0.687913268635 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1815851836968427 0.0 0.0 0.8318477057886463 0.0 0.0 0.0614355533687 1.0 0.562926629201 1.46183800752 0.759709693685 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1965599423868982 0.0 0.0 0.8522832650398825 0.0 0.0 0.0530887606352 1.0 0.529880891832 1.32766086401 0.818444892497 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2250345910622837 0.0 0.0 0.9031422438035661 0.0 0.0 0.0358542859262 1.0 0.547902297459 1.47814171752 0.912750355599 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

10500 15000

10500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.21633390118498 0.0 0.0 0.8948620958285299 0.0 0.0 0.0401199991983 1.0 0.513266581698 1.56055491306 0.889731899162 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

10500 15000

10500

10500 15000

10500

10500 15000

10500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1639725809550354 0.0 0.0 0.861563207034965 0.0 0.0 0.0511076520048 1.0 0.508252821459 1.70809254475 0.752707646017 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 10531 145 -936.8944437742232 -0.17110340396278123 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1778885179071212 0.0 0.0 0.8903860485463323 0.0 0.0 0.0503955179193 1.0 0.536147204816 1.61527253034 0.740991500033 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2304865718163687 0.0 0.0 0.8953972061113586 0.0 0.0 0.0264631090632 1.0 0.669220201153 1.5914334566 0.683981257664 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 10631 145 -1492.354594193476 1.7622455588550987 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2310856420304366 0.0 0.0 0.890638137106821 0.0 0.0 0.0303023462765 1.0 0.573536113477 1.31879333772 0.71692920286 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 10660 145 -972.0368174240654 1.129385159834368 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2310856420304366 0.0 0.0 0.890638137106821 0.0 0.0 0.0236740947281 1.0 0.508398059038 1.49405642596 1.09157697609 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1870891672116684 0.0 0.0 0.8529643666890564 0.0 0.0 0.0531146968245 1.0 0.538745324757 1.48230996309 0.708118882101 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1289202622460714 0.0 0.0 0.8002380477541213 0.0 0.0 0.071753546954 1.0 0.545189512832 1.37082529182 0.990493152219 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 10873 145 -795.8964165634471 0.48558607671527865 0.0

eta acceptanceProb 0 10894 145 -1238.4570426873336 0.709849812134014 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1115433085870332 0.0 0.0 0.7900917932397762 0.0 0.0 0.0805838620621 1.0 0.505949820569 1.79319010392 0.75529541832 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

11000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1653742333909574 0.0 0.0 0.8495741742479429 0.0 0.0 0.113101594093 1.0 0.501047167372 1.47064715959 0.751190799707 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

11000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1154910265376434 0.0 0.0 0.7609565149156082 0.0 0.0 0.114676705252 1.0 0.533557227264 1.66330005806 0.819683781396 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0986933888746349 0.0 0.0 0.7273233621870205 0.0 0.0 0.0916686410777 1.0 0.507589203422 1.91506863957 0.601135294581 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1041843215425162 0.0 0.0 0.7524961898703958 0.0 0.0 0.108597170375 1.0 0.594051324574 1.4203315606 0.825501032957 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.101869421025563 0.0 0.0 0.7650321450661097 0.0 0.0 0.11237584982 1.0 0.540329724496 1.59205130041 0.859863758255 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0660695990367688 0.0 0.0 0.7264048732778315 0.0 0.0 0.114903450839 1.0 0.528790640437 1.54449220849 0.788102986463 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.226033625918149 0.0 0.0 0.9135908041694161 0.0 0.0 0.100428642928 1.0 0.523953383643 1.46183350325 0.903382341395 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1991644441809672 0.0 0.0 0.8928294044928538 0.0 0.0 0.117462682194 1.0 0.513835437361 1.62411340472 0.92888065261 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1991644441809672 0.0 0.0 0.8928294044928538 0.0 0.0 0.118541920876 1.0 0.558495011089 1.85013517367 0.750594314462 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 11269 145 -788.7573712488954 -2.264926657179911 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2645837975511511 0.0 0.0 0.9563742249410878 0.0 0.0 0.0460113410553 1.0 0.53153923991 1.26991604179 0.964680316515 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1951626258017294 0.0 0.0 0.873273119576461 0.0 0.0 0.0819689547506 1.0 0.508807441134 1.28530921292 1.01090453287 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1951626258017294 0.0 0.0 0.873273119576461 0.0 0.0 0.0769009821179 1.0 0.565140222171 1.63966871315 1.01090453287 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.166065502494431 0.0 0.0 0.869778326695167 0.0 0.0 0.0616275863759 1.0 0.539014765822 1.30639357988 0.890029017608 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.117141722473772 0.0 0.0 0.8117297920042426 0.0 0.0 0.0669472100222 1.0 0.544572331263 1.40047435081 0.803219557397 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 11439 145 -826.5877037753389 1.8659604049332599 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1132893845200025 0.0 0.0 0.8092284912565044 0.0 0.0 0.0561713590302 1.0 0.536088101589 1.62850511697 1.07016020012 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

11500

eta acceptanceProb 0 11470 145 -892.8101194012673 2.6912390859841975 0.0

11500

eta acceptanceProb 0 11605 145 -1332.3406733481986 -3.997671815918369 0.0

eta acceptanceProb 0 11842 145 -755.0533427303355 1.254611327243725 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2147129161216486 0.0 0.0 0.9523638236640507 0.0 0.0 0.0844661909662 1.0 0.55603048119 1.36618008821 0.902527816133 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 11898 145 -755.6352753252412 -1.4170147111282394 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1744536122400504 0.0 0.0 0.8210132181251868 0.0 0.0 0.132663846274 1.0 0.522136373089 2.1186359384 0.624310789681 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

12000 15000

12000

12000 15000

12000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1827381829950547 0.0 0.0 0.9140961401734592 0.0 0.0 0.058909049225 1.0 0.676685152852 1.55889463071 0.747179791294 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

12000 15000

12000

12000 15000

12000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.147551259367477 0.0 0.0 0.8289515845627383 0.0 0.0 0.0678311682297 1.0 0.538341558264 1.47217697663 0.757422215226 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.168681780908252 0.0 0.0 0.8797605756713968 0.0 0.0 0.152488437027 1.0 0.530387882065 1.65699160862 0.842326864612 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1250734345547533 0.0 0.0 0.8791601239826627 0.0 0.0 0.115583339807 1.0 0.549122145296 1.74949133563 0.703229869307 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0370934240409355 0.0 0.0 0.8213591429455355 0.0 0.0 0.122812642206 1.0 0.500062567327 1.53676656328 0.783405107302 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0156867327874286 0.0 0.0 0.7924608485161777 0.0 0.0 0.0975568568247 1.0 0.518450656953 1.51033727305 0.65801268785 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1754123603651112 0.0 0.0 0.8729820277196545 0.0 0.0 0.0171815283303 1.0 0.536960257502 1.54457979964 0.915429570016 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2275037793851902 0.0 0.0 0.9510211275384995 0.0 0.0 0.0841813607861 1.0 0.506906900412 1.46381088051 0.623743803256 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

xi acceptanceProb 0 12210 116 -835.5690035811559 1.0416142078063706 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.171908685039734 0.0 0.0 0.8675349288590924 0.0 0.0 0.0153825832025 1.0 0.502850681506 1.37506870345 0.88772036621 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 12426 145 -1129.0991418669275 1.5037828961693362 0.0

eta acceptanceProb 0 12435 145 -777.2534640834089 1.0058875555867535 0.0

eta acceptanceProb 0 12445 145 -976.5470238901138 2.106782553103465 0.0

12500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1254523065949595 0.0 0.0 0.8040453956070731 0.0 0.0 0.0897293062531 1.0 0.522013053694 1.44347341745 0.637972152414 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

12500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1504529581539311 0.0 0.0 0.8133844765154996 0.0 0.0 0.10883659063 1.0 0.517812467947 1.5348240213 0.780170990732 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1161672504308133 0.0 0.0 0.7669663885051905 0.0 0.0 0.0884262788738 1.0 0.524231459129 1.74851738037 0.950673094712 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0514653392231974 0.0 0.0 0.7346390010872785 0.0 0.0 0.181981965053 1.0 0.506890129533 1.67179036753 0.685835937832 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 12774 145 -1491.861105100177 0.4745007182827883 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.200930676159648 0.0 0.0 0.8512341076030837 0.0 0.0 0.152734468189 1.0 0.506699046747 1.55379646338 0.736708365972 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0600140810941618 0.0 0.0 0.7958006281809787 0.0 0.0 0.110119812784 1.0 0.538066351271 1.58738903391 0.810188266832 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.105175961757441 0.0 0.0 0.8206570638497157 0.0 0.0 0.0979492405183 1.0 0.533193907973 1.66209009164 0.628868263571 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1855180502212783 0.0 0.0 0.8357172157077145 0.0 0.0 0.113819049029 1.0 0.570133520977 1.54052609813 0.721575048294 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1795194801820603 0.0 0.0 0.8932577148591073 0.0 0.0 0.0909923112063 1.0 0.558260642121 1.75040879533 0.775675533957 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1968830608011924 0.0 0.0 0.9030478109887838 0.0 0.0 0.0854465484038 1.0 0.505564491969 1.67831385074 0.739157590908 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

13000

eta acceptanceProb 0 12982 145 -959.921729326876 -0.2697318434146707 0.0

13000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.212181995380146 0.0 0.0 0.8840056264441308 0.0 0.0 0.121550422911 1.0 0.514465773783 1.75452555309 0.916663141149 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

13000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1231207545263118 0.0 0.0 0.7980798951889877 0.0 0.0 0.0724347929838 1.0 0.52723983421 1.64743940086 0.753263185709 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0950358924535224 0.0 0.0 0.7778024495601401 0.0 0.0 0.100870790176 1.0 0.521193674594 2.02365228046 0.657153711868 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1602389387112613 0.0 0.0 0.8052458196578365 0.0 0.0 0.0458236692858 1.0 0.507427719996 1.56765297025 0.702662326057 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1920693433686995 0.0 0.0 0.828024785659087 0.0 0.0 0.0531578116792 1.0 0.511938783606 1.66676941272 0.900567206631 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 13381 145 -1042.9769462015413 0.41065962171019743 0.0

eta acceptanceProb 0 13382 145 -839.452340734249 0.3150063245190753 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1583466503688125 0.0 0.0 0.8252700233011694 0.0 0.0 0.0474189910243 1.0 0.5019405514 1.56234954002 0.665272975901 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 13415 145 -842.3257084818545 -0.24033335855500182 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.137613672032306 0.0 0.0 0.8133476970036584 0.0 0.0 0.0928452407852 1.0 0.50636202727 1.78372417136 0.52455423031 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

13500 15000

13500

13500 15000

13500

13500 15000

13500

13500 15000

13500

14000

eta acceptanceProb 0 14046 145 -830.8166750190112 0.6434711032631371 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0255993714999525 0.0 0.0 0.7701980852162447 0.0 0.0 0.0842385962756 1.0 0.507101317151 1.85644114544 0.55041539293 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0823563298125134 0.0 0.0 0.7486650119102743 0.0 0.0 0.0334986559294 1.0 0.533413007243 1.41756004486 0.860693671123 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0905655037948572 0.0 0.0 0.734475469637931 0.0 0.0 0.0240744220441 1.0 0.516013664668 1.41526615283 0.798287851036 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2161340945892238 0.0 0.0 0.8730089943903385 0.0 0.0 0.0552911625896 1.0 0.605609735248 1.48602000552 0.729462043982 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2161340945892238 0.0 0.0 0.8730089943903385 0.0 0.0 0.0643441750182 1.0 0.503197810136 1.51705007765 0.673717239609 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1390105247984925 0.0 0.0 0.801360063558137 0.0 0.0 0.0415552550961 1.0 0.529897000039 1.73190187098 0.649602079202 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0850563389500143 0.0 0.0 0.7236281639226229 0.0 0.0 0.0479048814418 1.0 0.523889904352 1.43119479037 0.840741354211 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0850563389500143 0.0 0.0 0.7236281639226229 0.0 0.0 0.0487781781808 1.0 0.524281119208 1.40884334193 0.705639570021 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.224600711717256 0.0 0.0 0.9114500519020692 0.0 0.0 0.0715928705258 1.0 0.504194587396 1.80970883544 0.892980418136 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.224600711717256 0.0 0.0 0.9114500519020692 0.0 0.0 0.0743017339711 1.0 0.532374563648 1.95263522933 0.935013466028 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0949728269468266 0.0 0.0 0.7895582288123747 0.0 0.0 0.0204332252444 1.0 0.512224022047 1.33092349988 0.78193540048 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0949728269468266 0.0 0.0 0.7895582288123747 0.0 0.0 0.0236209130761 1.0 0.695854571741 1.59962427709 0.703927477323 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.22751167205933 0.0 0.0 0.9205819874520615 0.0 0.0 0.0904036841752 1.0 0.538791124057 1.55418293729 0.876257048473 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

14500

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.156762059540401 0.0 0.0 0.8490568238220983 0.0 0.0 0.102930642119 1.0 0.515760276284 1.71378335038 0.574024864601 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1505817165987307 0.0 0.0 0.8455800388043018 0.0 0.0 0.0512819125209 1.0 0.523480935861 1.91625278942 0.766282371492 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.0829322503363856 0.0 0.0 0.7555986478910841 0.0 0.0 0.1579632474 1.0 0.5530253258 1.85324626389 0.651041784808 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1655847175154124 0.0 0.0 0.8096206258811272 0.0 0.0 0.0932356038059 1.0 0.502507436684 1.70788329205 0.770191985475 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2022274281341518 0.0 0.0 0.9141049675632952 0.0 0.0 0.0596274334593 1.0 0.58365805546 1.68240533952 0.677565161136 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

eta acceptanceProb 0 14779 145 -1158.9401190030408 0.4652311131943965 0.0

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1509282977199797 0.0 0.0 0.8295144383010756 0.0 0.0 0.0306646742819 1.0 0.527510859146 1.56355757641 0.701865166869 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1520238460296215 0.0 0.0 0.8162606641096716 0.0 0.0 0.0512689238937 1.0 0.529353858137 1.73321797034 0.707250755484 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1535837347567346 0.0 0.0 0.8270115658373964 0.0 0.0 0.0339393817948 1.0 0.512722344558 1.66241840675 0.876273033895 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2096245095017832 0.0 0.0 0.8775892853234255 0.0 0.0 0.0561580114977 1.0 0.513573881667 1.69518060368 0.797794520225 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.0276121450505 1.0 0.536953411136 1.36069256366 0.854448197959 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.203040228412991 0.0 0.0 0.8641208209668187 0.0 0.0 0.0692674938149 1.0 0.506544742465 1.30351848394 0.845338576526 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.203040228412991 0.0 0.0 0.8641208209668187 0.0 0.0 0.0723532017328 1.0 0.6067184603 1.38981510345 0.845338576526 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.0229684949181 1.0 0.577760695775 1.55526808763 0.852772379029 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.203040228412991 0.0 0.0 0.8641208209668187 0.0 0.0 0.0831730392871 1.0 0.50679691511 1.38981510345 0.956670251653 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.0229684949181 1.0 0.548388802358 1.46159201454 1.07711242534 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1835672578885665 0.0 0.0 0.8456186195333008 0.0 0.0 0.0675881156931 1.0 0.518808554434 1.53832717662 0.816116974232 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.0173449168247 1.0 0.532370775305 1.26635432481 0.73752403593 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1437986802938176 0.0 0.0 0.8344951057571 0.0 0.0 0.068332441289 1.0 0.524729090323 1.55209080811 0.79150806072 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1437986802938176 0.0 0.0 0.8344951057571 0.0 0.0 0.0770307141789 1.0 0.521454336022 1.61706821386 0.85001190895 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1437986802938176 0.0 0.0 0.8344951057571 0.0 0.0 0.0740114773329 1.0 0.510997486303 1.28489398173 0.85001190895 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.016365296697 1.0 0.528730388627 1.14659120205 0.929716467162 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.1538709137581435 0.0 0.0 0.8421741388528898 0.0 0.0 0.0854594204524 1.0 0.564363605465 1.16914162566 0.747469706351 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

15000 15000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.023417008881 1.0 0.542838836877 1.21131934965 0.732885685796 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.023417008881 1.0 0.529204021769 1.21131934965 0.949743664582 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

15000 15000

prior_all_output == 0: ax3, bx3, s, k, p, m, s 1.2113496110010697 0.0 0.0 0.8755267401149827 0.0 0.0 0.023417008881 1.0 0.505659674198 1.16403132985 0.906857049022 PRIORS 2.5e-10 5e-09 0.2 0.2 0.0 0.1 0.1

acceptanceProb_pbad 1

15000 15000

/Users/lester/Documents/GitHub/kelly_model/scenario29/update_GBeagle_fix/true_constant_alpha/mass_sfr_z_correction/alpha_beta_covprop/random_start_gmm/random_start/bad_gmm_cuts/kelly_MH_GMM_3d_Hogg_constant_beta_constant_alpha.py:1693: RuntimeWarning: invalid value encountered in double_scalars

for key in keys:

Iteration: 15000

Rhat values:

alphaN: 1.0349268482844567 nan nan

beta: 1.0294726490629464 nan nan

sig0: 1.1298852697154662

k: nan

ximean: 1.0346329428043486

xivar: 0.9999856353312321

pbad: 1.0493600629325586

outlier_mean: 1.1341716247496667

outlier_sigma: 1.1336684172885223

_build_chain

In [2]: